Opticheskii Zhurnal. 2015. V. 82. № 8.

Production editor’s foreword

Potapov, A.S.

Generative and probability models of images in problems involving image processing and recognition

Iconics - the science of the image

Potapov, A.S.

Generative and probability models in image processing and computer vision

Lutsiv, V.R.

Convolutional deep-learning artificial neural networks

Malashin, R.O., Kadykov, A.B.

Investigation of the generalizing capabilities of convolutional neural networks in forming rotation-invariant attributes

Pang, Shuchao, Du, Anan, Yu, Zh. Zh.

Robust multi-object tracking using deep learning framework

Shcherbakov, O.V., Zhdanov, I.N., Lushin, Y.A.

A convolutional autoencoder as a generative model of images for problems of distinguishing attributes and restoring images in missing regions

Volynskiy, M.A., Gurov, I.P., Ermolaev, P.A., Skakov, P.S.

Comparative analysis of extended Kalman filtering and the sequential Monte Carlo method, using probability models of signals in optical coherent tomography

Skakov, P.S., Gurov, I.P.

Implementation of the sequential Monte Carlo method in systems with massive parallelism for processing images in optical coherent tomography

Filatov, V.I., Potapov, A.S.

Comparison of probabilistic programming languages, using the solution of clustering problems and the distinguishing of attributes as an example

Ponomarev, S.V.

Investigating surface-segmentation methods when images are being compared in three-dimensional space

Ivanov, P.I.

Using microelectromechanical systems when solving the problem of digital stabilization of video images

Zhdanov, I.N., Shcherbakov, O.V.

Modifying the Hough transform by using the periodicity of the regenerated structure of objects on an image

Maisheva, A.N.

Recognizing images with the help of color histograms